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Gift flowers should be chosen to depict a message with the sender's kansei and are bound by nature of flowers and social manners, to maintain social relationship between the sender and the recipient. Few buyers, but most florists, have expert knowledge of the flowering time, scent, price, and nature of each flower, and are experts in arranging flowers that meet a given purpose. The purpose of this paper is to incorporate handling constraints into the inference process of a kansei engineering system.

The paper collected the expert knowledge concerning nature of flowers, composing flower arrangements and social manners on gifts from specialists of flower arrangements including a florist and special books. At the same time, kansei evaluation experiments on the kinds of flowers and colors were conducted. The expertise and the results of kansei experiments were organized into a flower database and inference rules for choice of a main flower, arrangement shapes and combination flowers. The rules were implemented as server‐side programs. Users input information about the recipient, purpose of the arrangement and purchase information using a web browser. The system outputs a solution; a list of main flowers, combination flowers, greens and the shape of arrangement.

Traditional kansei engineering studies revealed the relationships between design elements and kansei with developing new analyzing methods. Different constraints come into the actual product design and manufacturing should be integrated with findings obtained from the kansei evaluation to successfully utilize kansei engineering for product development.

The inference rules will be able to tell the reasons for choosing the main‐ and combination flowers and arrangement shapes to satisfy the customers.

The proposed system suggests the original arrangement of flowers unlike most online florists selling ready‐made arrangements. The paper shows a solution to incorporate different constraints underlying in a real production process into the inference process based on the result of kansei analysis.